
SAP MENA
Senior Machine Learning Engineer
- Permanent
- Dubai, United Arab Emirates
- Experience 5 - 10 yrs
Job expiry date: 27/05/2026
Job overview
Date posted
13/04/2026
Location
Dubai, United Arab Emirates
Salary
Undisclosed
Compensation
Comprehensive package
Job description
The Senior Machine Learning Engineer role at SAP in Dubai focuses on designing and delivering production-grade machine learning and generative AI systems that operate at scale across enterprise environments. The position involves owning end-to-end ML architecture including data pipelines, feature stores, model training workflows, serving infrastructure, online experimentation, and observability systems. The role is responsible for building advanced AI solutions across deep learning, NLP, ranking, recommendation, forecasting, semantic retrieval, and large language model (LLM) applications. This includes implementing generative AI systems using embeddings, vector databases, retrieval-augmented generation (RAG) pipelines, agent-based workflows, prompt engineering, and safety guardrails. The engineer drives technical decisions on distributed training and inference, GPU optimization, model compression, retrieval optimization, drift detection, retraining strategies, and responsible AI governance. The role requires deep expertise in MLOps and platform engineering, including CI/CD pipelines, feature stores, model registries, experiment tracking, and automated validation frameworks. The engineer ensures performance, scalability, reliability, latency, and cost efficiency in live production environments. The position also includes defining evaluation frameworks, including offline benchmarking, online experimentation, hallucination analysis, and model risk assessment. Additionally, the role involves mentoring engineers, establishing reusable engineering patterns, and contributing to AI platform capabilities within SAP’s enterprise cloud ecosystem serving global customers across industries.
Required skills
Key responsibilities
- Design and implement production-grade machine learning and generative AI systems including data pipelines, feature stores, training workflows, and model-serving infrastructure
- Develop and deploy advanced AI solutions across NLP, deep learning, ranking, recommendation, forecasting, and semantic retrieval use cases
- Build and optimize generative AI applications using embeddings, vector databases, RAG pipelines, agent workflows, prompt engineering, and safety controls
- Define and implement MLOps frameworks including CI/CD pipelines, model registries, feature stores, experiment tracking, and automated validation systems
- Lead architecture decisions for distributed training and inference systems including GPU utilization, model compression, and scalability optimization
- Implement model evaluation and monitoring systems including offline benchmarking, online experimentation, drift detection, and performance observability
- Drive responsible AI practices including hallucination analysis, model risk assessment, governance controls, and safety mechanisms
- Mentor engineers and contribute to reusable platform capabilities, engineering standards, and best practices for enterprise-scale AI systems
Experience & skills
- Demonstrate 7–9+ years of experience in software engineering and machine learning with production-scale AI systems
- Possess expert-level programming skills in Python and strong proficiency in Java or Go
- Show deep knowledge of machine learning, deep learning, NLP, ranking, and recommendation systems
- Demonstrate extensive experience building end-to-end ML systems including data ingestion, training, deployment, and observability
- Exhibit strong hands-on experience with ML frameworks such as PyTorch, TensorFlow, and scikit-learn
- Show practical experience building generative AI systems including RAG pipelines, embeddings, vector databases, and LLM-based applications
- Demonstrate strong expertise in MLOps including CI/CD, feature stores, model registries, and experiment tracking
- Possess architectural knowledge of distributed systems, streaming data, event-driven services, and cloud-native ML platforms
- Demonstrate ability to design evaluation frameworks including benchmarking, online experimentation, and responsible AI governance